VOCs监测技术及BP神经网络预测模型在船舶涂装车间的应用

Application of VOC Monitoring Technologies and BP Neural Network Prediction Models in Ship Painting Workshops

VOCs污染问题是我国环境保护遇到的最大问题之一。控制和减少VOCs的排放,既是日趋严格的环保标准的要求,也是建设船舶行业绿色生产体系的内在需求。不同环境下的VOCs监测,其内涵、监测项目、监测方法等存在差异又极易混淆。梳理了VOCs监测和浓度分布的有关概念,结合某船厂涂装车间的具体工况,给出了船舶涂装车间排放VOCs的监测方案以及VOCs监测场景下的PID检测器技术应用和在线监测系统的构成,采用神经网络方法构建了监测点VOCs数据与涂装车间空间中任一点VOCs数据之间的关系模型,为实现互联互通绿色制造车间提供了数据支撑。

The problem of VOCs(volatile organic compounds)pollution is one of the significant challenges encountered in China's environmental protection.Controlling and reducing the emission of VOCs is not only a requirement of the increasingly strict environmental protection standards but also an inherent requirement for building a green production system in the shipbuilding industry.The monitoring of VOCs in different environments entails differences in terms of connotations,monitored elements and monitoring methods,which can lead to confusion.In this paper,the relevant concepts of VOC monitoring and concentration distribution were clarified.Based on the specific working conditions of a certain shipyard’s painting workshop,a monitoring scheme for VOCs emissions from the ship’s painting workshop was provided,as well as the application of PID sensor technology and the composition of an online monitoring system in the VOCs monitoring scenario.A neural network approach was utilized to construct a relationship model between the VOCs data collected at a monitoring point and the VOCs data from any location within the painting workshop space.Data support was provided for the implementation of a connected green manufacturing workshop.